Vetted seaborn Professionals

“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”

“Built and deployed a production RAG-based internal document Q&A system using LangChain, vector search, and a dockerized FastAPI LLM service. Focused on reliability by systematically reducing hallucinations and improving retrieval through prompt grounding/abstention strategies, chunking and top-k tuning, and iterative evaluation with logged metrics and manual validation.”

“AI/LLM engineer who has shipped production RAG and agentic systems end-to-end (LangChain/FAISS, OpenAI+Gemini, FastAPI, Docker, Streamlit), focusing on retrieval quality and low-latency performance. Also partnered with a non-technical PM at deepNow to deliver a forecasting + summarization pipeline for daily market insights with iterative prototyping and a simple UI.”

“LLM/RAG engineer at Connex AI who built and deployed a production healthcare agent to extract clinical insights from medical data/notes. Strong focus on real-world reliability—hallucination mitigation (citations, schema validation, confidence thresholds, rejection logic), custom LangChain orchestration (query rewriting, fallback paths), and production evaluation/observability—while collaborating closely with clinical SMEs to ensure clinical fit and time savings.”

“Research Analyst at Syracuse who deployed an LLM-powered lab automation system allowing researchers to run QCoDeS instrument workflows via natural language, with strong safety guardrails for real instruments and multi-instrument support. Also collaborated with non-technical stakeholders at iConsult on an audio classification/recommendation pipeline, translating business goals into metrics and Tableau dashboards with model comparisons and A/B test results.”

“Backend/ML engineer with healthcare domain experience building secure Medicare/Medicaid data APIs and real-time patient risk scoring. Shipped an end-to-end ML pipeline (scikit-learn/XGBoost) served via SageMaker and integrated into Flask APIs, with strong production reliability practices (Kafka schema validation, regression replay, observability, drift monitoring, and human-in-the-loop guardrails).”

“Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.”

“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”

“Backend engineer building an AI-powered social media platform on AWS, with hands-on experience shipping LLM-backed application features and improving production performance under high traffic. Strong focus on reliability/observability (CloudWatch, structured logs, health checks) and database optimization (MongoDB explain/slow logs, indexing, caching, connection pooling).”